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In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods , LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs(More)
  • Elhanan Helpman, Oleg Itskhoki, Marc-Andreas Muendler, Stephen Redding, Sam Bazzi, Lorenzo Casaburi +32 others
  • 2012
While neoclassical theory emphasizes the impact of trade on wage inequality between occupations and sectors, more recent theories of firm heterogeneity point to the impact of trade on wage dispersion within occupations and sectors. Using linked employer-employee data for Brazil, we show that much of overall wage inequality arises within sector-occupations(More)
Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to provide domain knowledge with extra rewards to converge faster. The reward shaping functions are normally defined in advance by the user and are static. This paper introduces a(More)
Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficult to solve with existing reinforcement learning methods due to the large search space. In this paper, we use a relational representation to define powerful abstractions that allow(More)
Segmentation through seeded region growing is widely used because it is fast, robust and free of tuning parameters. However, the seeded region growing algorithm requires an automatic seed generator, and has problems to label unconnected pixels (unconnected pixel problem). This paper introduces a new automatic seeded region growing algorithm called ASRG-IB1(More)
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but(More)
In the market for depression care, physicians face uncertainty about how a newly diagnosed patient will respond to available treatments. I design a framework to analyze how price and drug promotion influence the physician's recommendation as she sequentially searches for the best match. Unlike earlier work on learning, I allow the physician to update her(More)